The Law of Large Documents: Understanding the Structure of Legal Contracts Using Visual Cues

TL;DR

This paper demonstrates that incorporating visual cues like layout and style significantly improves the understanding of long legal documents by using computer vision techniques for segmentation and information extraction.

Contribution

The study introduces a novel approach that leverages visual cues for segmenting and understanding long legal documents, outperforming existing methods.

Findings

Visual cues improve document segmentation accuracy

Enhanced understanding of legal contracts using visual features

Outperforms existing methods on four long-document tasks

Abstract

Large, pre-trained transformer models like BERT have achieved state-of-the-art results on document understanding tasks, but most implementations can only consider 512 tokens at a time. For many real-world applications, documents can be much longer, and the segmentation strategies typically used on longer documents miss out on document structure and contextual information, hurting their results on downstream tasks. In our work on legal agreements, we find that visual cues such as layout, style, and placement of text in a document are strong features that are crucial to achieving an acceptable level of accuracy on long documents. We measure the impact of incorporating such visual cues, obtained via computer vision methods, on the accuracy of document understanding tasks including document segmentation, entity extraction, and attribute classification. Our method of segmenting documents based on structural metadata out-performs existing methods on four long-document understanding tasks as measured on the Contract Understanding Atticus Dataset.